9 results about "Service placement" patented technology

The present invention discloses a service placement and task offloading strategy method and system for edge environments based on computational reuse. This method addresses the quality of service (QoS) issues associated with computing services that are frequently requested by users in resource-constrained edge environments. It divides the service request processing model into three parts: one part is processed through computational reuse, another part is processed by edge servers, and the remaining part is handled by cloud servers. A reasonable service placement model is also established, enabling more requests to be served within limited edge resources, thereby fully utilizing edge resources and improving overall service quality.

The invention discloses a combined service placement, task scheduling and resource allocation method in a multi-sensor multi-user edge computing network. The method comprises the following steps: constructing a multi-sensor multi-user edge computing system model; establishing a user task processing and sensor data collection delay and energy consumption model, and further establishing a system overhead optimization problem; an optimization problem is decomposed, and a user and sensor transmitting power allocation decision and a computing resource and transmission rate allocation decision are solved based on a numerical optimization algorithm; a service placement and task scheduling sub-problem is constructed into a dual-time-scale Markov decision process, and a combined service placement, task scheduling and resource allocation strategy is trained and applied based on a deep reinforcement learning technology. According to the method, factors such as the user task arrival rate, the sensor data size and the edge server resources are comprehensively considered, service placement, task scheduling and resource allocation decision are jointly optimized, efficient operation of the edge system is guaranteed, and user experience is improved.

A method for an energy-efficient service placement in a mobile edge cloud comprising at least one edge site is disclosed. The method comprises receiving a service placement request from a service provider. The method comprises identifying a set of candidate edge site groups and calculating a first energy efficiency value for each identified candidate edge site group. Further, the method comprises calculating a second energy efficiency value for components of the cellular network that are involved in the communication between the user device and the edge site. The method comprises determining for each candidate edge site group, an energy efficiency metric for deploying said service placement request in a traffic path of the cellular network based on the first and second energy efficiency values. The method further comprises determining a service placement policy for the service placement based on the calculated energy efficiency metric and the obtained performance parameters.

A method for an energy-efficient service placement in a mobile edge cloud comprising at least one edge site is disclosed. The method comprises receiving a service placement request from a service provider. The method comprises identifying a set of candidate edge site groups and calculating a first energy efficiency value for each identified candidate edge site group. Further, the method comprises calculating a second energy efficiency value for components of the cellular network that are involved in the communication between the user device and the edge site. The method comprises determining for each candidate edge site group, an energy efficiency metric for deploying said service placement request in a traffic path of the cellular network based on the first and second energy efficiency values. The method further comprises determining a service placement policy for the service placement based on the calculated energy efficiency metric and the obtained performance parameters.

The present invention discloses a task-aware service placement method for distributed learning in a wireless edge network. The present invention places services for distributed learning applications based on a service directed acyclic graph with resource and communication demand information obtained by merging the original task directed acyclic graph. This step includes the following two stages: the first stage is a stage of searching for server nodes to be placed for each independent service directed acyclic graph after merging; the second stage is a stage of evaluating the overall delay of a given service placement scheme; by iterating the above two steps, the optimal distributed learning application service placement scheme is found. In response to the problems of unreliable communication, random user movement, and service queuing, the present invention proposes a task-aware service placement method that takes into account the dependencies between placed services, thereby reducing the expected delay of all learning tasks and improving the task completion rate of the edge network. The present invention is applicable to the field of edge computing.

Methods, systems, and computer program products for cold-start service placement over on-demand resources are provided herein. A computer-implemented method includes obtaining a performance requirement profile comprising performance requirements of a service that vary over time; determining a plurality of incarnations for the service, wherein each incarnation is associated with a level of performance provided by the incarnation for the service, resource requirements of the incarnation, and a type of computing node the incarnation is configured to execute on; identifying computing nodes having different types and different resource capacities; jointly scheduling (i) the computing nodes and (ii) one or more of the incarnations on the computing nodes over a time interval such that a cumulative level of performance of the incarnations scheduled at each timepoint in the time interval satisfies the performance requirement profile of the service.

The invention belongs to the technical field of wireless communication, and discloses a marginal air-ground network optimization method and system based on hierarchical deep reinforcement learning (joint optimization method and system), and the optimization of the marginal air-ground network is realized through joint optimization of a service placement strategy, UAV trajectory planning, access control, bandwidth allocation and transmission power. And efficient equipment connection and data processing services are provided for disaster area rescue, urban intelligent monitoring and other scenes. According to the method, a dual-time-scale optimization strategy is adopted: on a coarse-grained frame-level time scale, a high-level agent adopts a deep Q network (DQN) proxy, and a placement strategy of service in an MEC server is decided according to a UAV task request to ensure that resource constraints are met; on the fine-grained time slot level time scale, a low-layer agent adopts an improved depth deterministic policy gradient (IDDPG) for proxy, the UAV trajectory, TDMA-based access control, bandwidth allocation proportion and transmission power are optimized, and the real-time task requirement is met. Through the layered framework, the system can improve the task success rate, the energy efficiency and the resource scheduling fairness in a dynamic environment, and efficient and stable air-ground integrated network service is realized.

This disclosure describes techniques and mechanisms for utilizing affinity routing in SDWAN networks. The techniques may enable network administrators to assign and / or configure affinity numbers to hub(s) and / or gateway(s), tunneling interface(s), service(s), etc., as well as affinity-preference-order(s) to edge device(s) within the network. Network administrators may also configure control polic(ies). The techniques enable a scalable and simplified way to automatically load-balance traffic across different gateways within a network, while reducing network resource usage. The techniques may utilize routing affinity to achieve a variety of networking related functionalities, including automatic load-balancing of traffic, provisioning of active and backup gateways, optimal route distribution to routers from routing controllers, optimized service placement for edge routers, without the need for any policy configuration at all, let alone complex policies.

The present application provides a method and apparatus for dynamic expansion and placement of edge computing services, the method comprising: automatically expanding the number of microservice replicas for target data processing requests based on the current workload intensity prediction results of each microservice corresponding to each application in the edge computing platform and the current work performance evaluation results of each edge node, so as to determine the scaled optimized number of microservice replicas; using a preset adaptive discrete binary particle swarm optimization algorithm, according to the scaled optimized number of microservice replicas, the current number of placeable edge nodes, and performance information, obtaining the mapping relationship between each microservice replica and each available edge node, so as to place each microservice replica to the corresponding edge node. The present application can improve the reliability and effectiveness of automatic expansion of edge computing services in edge environments where edge load is unbalanced and network status is unreliable, and improve the accuracy and reliability of edge computing service placement.